Gait analysis has many applications ranging from rehabilitation to sports medicine, orthopedics and studying the effectiveness of prosthetics to improve their design. See Joseph C, Andrew G., “Gait Analysis in the Amputee: Has it Helped the Amputee or Contributed to the Development of Improved Prosthetic Components?” Gait Posture (1996) 4, 258-68, of which is hereby incorporated by reference herein in its entirety. Long-term in-home gait monitoring not only can provide a measure of a person's functional ability, but it also can provide a measure of activity levels and may therefore help ‘evaluate’ a person's health over a long period of time. Passive in-home gait monitoring can be useful for assessing healing/deterioration following therapeutic interventions including surgeries, drug or physical therapy. Moreover, the ability to identify negative trends of subtle changes in a person's gait can contribute to detection of health problems at early stages of disease onset. Research also indicates that certain gait characteristics can be used as a biometric for identification purposes. See Little J, Boyd J., “Recognizing People by Their Gait: the Shape of Motion,” Videre, Winter 1998, of which is hereby incorporated by reference herein in its entirety. See Orr R, Abowd G., “The Smart Floor: A Mechanism for Natural User Identification and Tracking Conference on Human Factors in Computing Systems,” April 2000, of which is hereby incorporated by reference herein in its entirety.
On the other hand, falls are a major cause of morbidity in the elderly. See François P, Hélène C, Réjean H, David W., “Gait in the Elderly,” Gait and Posture (1997) 5(2), 128-135, of which is hereby incorporated by reference herein in its entirety. They are responsible for 70 percent of accidental deaths in persons 75 years of age and older. The elderly, who represent 12 percent of the population, account for 75 percent of deaths from falls. See George F., “Falls in the Elderly,” American Family Physician, April 2000, of which is hereby incorporated by reference herein in its entirety. The considerable cost involved in the treatment and Hospitalization of fall injuries and even death due to falls could be greatly reduced if falls could be predicted and avoided through appropriate intervention. An in-home gait-monitoring tool with the ability to distinguish between normal walking and abnormal gait may help predict a propensity for injurious falls. See Stalenhoef P A, Diederiks J P, Knottnerus J A, Kester A D, Crebolder H F., “A Risk Model for the Prediction of Recurrent Falls in Community-Dwelling Elderly: a Prospective Cohort Study,” J Clin Epidemiol 2002 November; 55(11):1088-94, of which is hereby incorporated by reference herein in its entirety. See Azizah Mbourou G, Lajoie Y, Teasdale N., “Step Length Variability at Gait Initiation in Elderly Fallers and Non-Fallers, and Young Adults,” Gerontology. 2003 January-February; 49(1):21-6, of which is hereby incorporated by reference herein in its entirety.
Human gait analysis entails numerous parameters that can be classified into spatio-temporal, kinematic and kinetic characteristics. Spatio-temporal parameters include average walking velocity, stride length, step length, step time, cadence, stance phase time, swing phase time, single support (when only one foot is in contact with the floor), double support (when both feet are in contact with the floor), and stride width. Kinematic parameters study the angles between the ankle, hip and knee joints. Finally, kinetic analysis examines moments, energy and power at these joints. See Craik R, Oatis C., “Gait Analysis Theory and Application,” Mosby 1995, of which is hereby incorporated by reference herein in its entirety.
Most gait analysis laboratories use visual means for gait analysis where kinematic (See Dockstader S, Tekalp A., “A Kinematic Model for Human Motion and Gait Analysis,” Proc. of the Workshop on Statistical Methods in Video Processing (ECCV), Copenhagen, Denmark, 1-2 Jun. 2002, pp. 49-54, of which is hereby incorporated by reference herein in its entirety) and biomechanical models (See Simon J, Metaxiotis D, Siebel A, Bock H, Döderlein L., “A Multi-Segmented Foot Model,” 6th Annual Gait and Clinical Movement Analysis'Meeting, Shriners Hospitals for Children, Northern California, of which is hereby incorporated by reference herein in its entirety) are built from visually acquired gait data. A review of the various visual human motion and gait analysis techniques can be found in the Aggarwal J, and Cai Q. article (See Aggarwal J, Cai Q., “Human Motion Analysis: A Review,” Proceedings, IEEE Nonrigid and Articulated Motion Workshop, June 1997, of which is hereby incorporated by reference herein in its entirety.) and Gavrila D. article (See Gavrila D., “The Visual Analysis of Human Movement: A Survey,” Computer Vision and Image Understanding, 73(1): 82-98, January 1999, of which is hereby incorporated by reference herein in its entirety.) An excellent overview of in-the-lab gait analysis tools, methods and applications in rehabilitation can be found in the DeLisa J. article (See DeLisa J., “Gait Analysis in the Science of Rehabilitation,” VARD Monograph 002, 1998, of which is hereby incorporated by reference herein in its entirety). Gait lab equipment and analysis techniques yield excellent and detailed gait characteristics and enable clinicians to prescribe an appropriate intervention. However, the equipment required for a functional gait laboratory is extremely expensive, in the range of tens of thousands to a few hundred thousand dollars in equipment and software. Additionally, the computational power required for the image based analysis make longitudinal in-home gait monitoring using these technologies impractical. Moreover, people are normally referred to gait labs for full gait analysis only after the changes in their gait have become obvious. Gait Laboratories also use pressure measurement systems such as force plates for gait analysis. Force plate data can reveal important information such as a quantitative evaluation of the effect of Total Knee Arthoplasty (TKA) in patients with osteoarthritis. See Otsuki T, Nawata K, Okuno M., “Quantitative Evaluation of Gait Pattern in Patients With Osteoarthritis of the Knee Before and After Total Knee Arthoplasty. Gait Analysis Using a Pressure Measuring System,” Journal of Orthopaedic Science, 4(2): 99-105, 1999, of which is hereby incorporated by reference herein in its entirety. The pressure system measured Stance phase timing and forces. However, research at the Ohio State University demonstrated that force plate size influenced valid gait data acquisition (See Oggero E, Pagnacco G, Morr D R, Berme N., “How Force Plate Size Influences the Probability of Valid Gait Data Acquisition,” Biomedical Sciences Instrumentation, 35:3-8 1999, of which is hereby incorporated by reference herein in its entirety) and that some subjects must alter their gait for valid gait data acquisition (See Oggero E, Pagnacco G, Morr D R, Simon S R, Berme N., “Collecting Valid Data From Force Plates: How Many Subjects Must Alter Their Gait?” North American Congress on Biomechanics 2000, of which is hereby incorporated by reference herein in its entirety).
Current outside the lab gait analysis techniques broadly fall under three categories depending upon the type of device used, wearable devices, walk on devices and visual gait analysis tools and techniques. Wearable devices include actigraphs and devices such as that described in the gait activity monitor to Smith et al. (See U.S. Pat. No. 5,485,402 to Smith et al., entitled “Gait Activity Monitor,” of which is hereby incorporated by reference herein in its entirety.) These devices measure acceleration resulting from movement of the body or limb that may not necessarily correspond to walking. Moreover, accelerometers do not provide enough information to enable the evaluation of the actual characteristics of the gait. The gait activity monitor described in Weir et al. (See U.S. Pat. No. 5,831,937 to Weir et al., entitled “Portable Ranging System for Analyzing Gait;” of which is hereby incorporated by reference herein in its entirety.) is worn on the ankle with built-in optical communication. Another variation on this type of devices is described in Portable Ranging System, where a combination of ultrasound and infrared is used to periodically determine the distance between a person and a base station (See U.S. Pat. No. 5,623,944 to Nashner, entitled “Method for Characterizing Gait,” of which is hereby incorporated by reference herein in its entirety; this device is mainly used to measure velocity). Walk-on gait analysis devices include treadmills (See U.S. Pat. No. 5,952,585 to Trantzas et al., entitled “Portable Pressure Sensing Apparatus for Measuring Dynamic Gait Analysis and Method of Manufacture;” of which is hereby incorporated by reference herein in its entirety), mats (See U.S. Pat. No. 6,360,597 B1 to Hubbard, Jr., entitled “In-Shoe Remote Telemetry Gait Analysis System, of which is hereby incorporated by reference herein in its entirety), special shoes (See Classification of Gait Abnormalities: http://guardian.curtin.edu.au/cga/faq/classification.html, of which is hereby incorporated by reference herein in its entirety), and specially designed floors (See Orr R, Abowd G., “The Smart Floor: A Mechanism for Natural User Identification and Tracking Conference on Human Factors in Computing Systems,” April 2000, of which is hereby incorporated by reference herein in its entirety). The treadmill described in ‘Method for characterizing gait’ (See Gavrila D., “The Visual Analysis of Human Movement: A Survey,” Computer Vision and Image Understanding, 73(1): 82-98, January 1999, of which is hereby incorporated by reference herein in its entirety.) has transducers mounted below the movable surface that can measures force from each foot individually can differentiate between walking and running. Arrays of pressure sensors are placed under a flexible mat sheet are described in (See U.S. Pat. No. 5,952,585, of which is hereby incorporated by reference herein in its entirety.) to measure force and other gait parameters. Another approach (See U.S. Pat. No. 6,360,597, of which is hereby incorporated by reference herein in its entirety), describes an in-shoe pressure sensing system with an external telemetry transmitter. The pressure sensor data is transmitted to a remote computer for analysis. Another potential method for gait analysis is to have a ‘smart floor’ comprising force plate tiles or embedding load cells under individual tiles (See Orr R, Abowd G., “The Smart Floor: A Mechanism for Natural User Identification and Tracking Conference on Human Factors in Computing Systems,” April 2000, of which is hereby incorporated by reference herein in its entirety.) to measure characteristics of footsteps; this approach is expensive.